Relative to the original Mask R-CNN in [1]}, we modernize several of its modules. Concisely, the modifications include: (1) following the convolutions in FPN with batch normalization (BN) [2]}, (2) using two convolutional layers in the region proposal network (RPN) [3]} instead of one, (3) using four convolutional layers with BN followed by one linear layer for the region-of-interest (RoI) classification and box regression head [4]} instead of a two-layer MLP without normalization, (4) and following the convolutions in the standard mask head with BN. Wherever BN is applied, we use synchronous BN across all GPUs. These upgrades are implemented in the Detectron2 model zoo.https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md#new-baselines-using-large-scale-jitter-and-longer-training-schedule
